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Computational Experiences with Branching on Hyperplane Algorithm for Mixed Integer Programming

Computational Experiences with Branching on Hyperplane Algorithm for Mixed Integer Programming. Sanjay Mehrotra Department of IE/MS Northwestern University Evanston, IL 60208 Joint Work with Zhifeng Li and Huan-yuan Sheng. Organization. Introduction and Review Adjoint Lattice

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Computational Experiences with Branching on Hyperplane Algorithm for Mixed Integer Programming

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  1. Computational Experiences with Branching on Hyperplane Algorithm for Mixed Integer Programming Sanjay Mehrotra Department of IE/MS Northwestern University Evanston, IL 60208 Joint Work with Zhifeng Li and Huan-yuan Sheng

  2. Organization • Introduction and Review • Adjoint Lattice • Theoretical Results • Computational Results • Conclusions

  3. Problem Formulation

  4. Approaches to Branching • Branching Strategies • Branching on variables • Most infeasible branching • Strong branching • Nested Cluster Branching • Pseudo-cost branching • Branching on constraints • Branching on variables may not be efficient for solving general MIP.

  5. An Example

  6. Example (continued)

  7. An Example (continued)

  8. Branching on Hyperplane Algorithm(s) Lenstra’s Algorithm

  9. Algorithm Continued… Generalized Basis Reduction in Lovasz-Scarf Algorithm

  10. Branching on hyperplane Algorithm Step 2: Step 3:

  11. Pervious Work • Lenstra [83] proposed algorithm branching on hyperplane • Grötschel, Lovász and Schrijver [84] used ellipsoidal approximation directly. • Lovász and Scarf [92] developed Generalized Basis Reduction (GBR) algorithm that does not require ellipsoidal approximation and works with the original model, however, assumes full dimensionality. • Cook, et. al. [93] implemented GBR algorithm for some hard network design problems. • Wang [97] implemented GBR algorithm for LP and NLP (<100 integer variables). • Aardal, Hurkens, and Lenstra [98,00], Aardal et. al. [00], Aardal and Lenstra [02], proposed a reformulation technique and solved some hard equality constrained integer knapsack and market split problems using this reformulation. • Owen and Mehrotra [02] computationally showed that good branching disjunctions are available using heuristics for smaller problems in the MIPLIB testset. • Gao and Zhang [02] implemented Lenstra's algorithm and tested an interior point algorithm for finding the maximum volume ellipsoid.

  12. Issues • Dimension reduction… hmm… • Don’t know what to do when continuous variables are present • Continuous variables are “projected” out • How to do this for the more general convex problem? • It is just nice to work in the original space! • Basis Reduction at every node… • Finding a feasible solution or • finding good branching directions • Ellipsoidal Approximation • We should use the modern technologies! • Feasibility problem vs. Optimization Problem • Disjunctive branching (instead of branching on hyperplanes)

  13. Matter of Definitions

  14. Adjoint Lattice

  15. Branching Hyperplane finding Problem

  16. Find An integral Adjoint Basis

  17. How to Solve Minimum Width Problems

  18. Lovasz and Scarf Basis

  19. Mixed Integer Programming • Feasibility Mixed Integer Linear Program (FMILP) is to

  20. Mixed Integer Problem

  21. Generalized B&B Method IMPACT (Integrated Mathematical Programming Advanced Computational Tool) implementation of GBB. • A growing search tree

  22. Knapsack Test Problems

  23. Numerical Results on Hard Knapsack Problems

  24. Market Split Problems

  25. Market Split Problems

  26. Larger Market Split Problems

  27. # of LLL iterations and GBB Tree Size

  28. Work in progress It is possible to develop stable codes for Dense Difficult Market Split problems using Adjoint Lattice Basis. Increasing the swap threshold in LLL algorithm reduces the total number of branches, but this is not consistent beyond .99 GBR algorithm in Lova’sz-Scarf branching on hyperplane algorithm is sensitive to optimality precision. Conclusions

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